带重置的条件累加 [英] Conditional cumsum with reset

问题描述

我有一个数据框，该数据框已经根据需要进行了排序，但现在我想将其切片"成组.

I have a data frame, the data frame is already sorted as needed, but now I will like to "slice it" in groups.

该组的最大累计值应为10，当累计值>10时，应重置累计总和并重新开始

This groups should have a max cumulative value of 10. When the cumulative value is > 10, it should reset the cumulative sum and start over again

library(dplyr)
id <- sample(1:15)
order <- 1:15
value  <- c(4, 5, 7, 3, 8, 1, 2, 5, 3, 6, 2, 6, 3, 1, 4)
df  <- data.frame(id, order, value)
df

这是我正在寻找的输出(我是手动"完成的)

This is the output I'm looking for(I did it "manually")

cumsum_10  <- c(4, 9, 7, 10, 8, 9, 2, 7, 10, 6, 8, 6, 9, 10, 4)
group_10 <- c(1, 1, 2, 2, 3, 3, 4, 4, 4, 5, 5, 6, 6, 6, 7)
df1  <- data.frame(df, cumsum_10, group_10)
df1

所以我有两个问题

1. 如何创建每次超过上限(在本例中为 10)时重置的累积变量
2. 如何对每组进行计数/分组

对于第一部分，我尝试了 group_by 和 cumsum 的一些组合，但没有运气

For the first part I was trying some combinations of group_by and cumsum with no luck

df1 <- df %>% group_by(cumsum(c(False, value < 10)))

我更喜欢管道 (%>%) 解决方案而不是 for 循环

I would prefer a pipe (%>%) solution instead of a for loop

谢谢

推荐答案

我认为这不容易矢量化......至少我不知道如何.

I think this is not easily vectorizable.... at least i do not know how.

您可以通过以下方式手动:

my_cumsum <- function(x){
  grp = integer(length(x))
  grp[1] = 1
  for(i in 2:length(x)){
    if(x[i-1] + x[i] <= 10){
      grp[i] = grp[i-1]
      x[i] = x[i-1] + x[i]
    } else {
      grp[i] = grp[i-1] + 1
    }
  }
  data.frame(grp, x)
}

对于您的数据，这给出:

For your data this gives:

> my_cumsum(df$value)
   grp  x
1    1  4
2    1  9
3    2  7
4    2 10
5    3  8
6    3  9
7    4  2
8    4  7
9    4 10
10   5  6
11   5  8
12   6  6
13   6  9
14   6 10
15   7  4

对于我的反例"，这给出了:

Also for my "counter-example" this gives:

> my_cumsum(c(10,6,4))
  grp  x
1   1 10
2   2  6
3   2 10

正如@Khashaa 指出的，这可以通过 Rcpp 更有效地实现.他链接到这个答案 如何加速或向量化 for 循环? 我觉得这非常有用

As @Khashaa pointed out this can be implementet more efficiently via Rcpp. He linked to this answer How to speed up or vectorize a for loop? which i find very useful

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